SUMMARY OUTPUT Regression Statistics Multiple R 0.303005183 R Square 0.091812141 Adjusted R Square 0.05937686 Standard Error 8.538049739 Observations 30 ANOVA Significance F df Regression 1. 206.3477865 206.347787 2.83062575 0.103603045 Residual 28 2041.152213 72.8982933 Total 29 2247.5 t Stat Coefficients Standard Error P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 1.872650091 8.248361046 0.22703299 0.82204756 -15.02335158 18.7686518 -15.02335158 18.76865177 #Flashdrives 1.937537902 1.15161942 1.68244636 0.10360305 -0.421447544 4.29652335 -0.421447544 4.296523347

SUMMARY OUTPUT Regression Statistics Multiple R 0.303005183 R Square 0.091812141 Adjusted R Square 0.05937686 Standard Error 8.538049739 Observations 30 ANOVA Significance F df Regression 1. 206.3477865 206.347787 2.83062575 0.103603045 Residual 28 2041.152213 72.8982933 Total 29 2247.5 t Stat Coefficients Standard Error P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 1.872650091 8.248361046 0.22703299 0.82204756 -15.02335158 18.7686518 -15.02335158 18.76865177 #Flashdrives 1.937537902 1.15161942 1.68244636 0.10360305 -0.421447544 4.29652335 -0.421447544 4.296523347

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter5: Inverse, Exponential, And Logarithmic Functions

Section5.6: Exponential And Logarithmic Equations

Problem 67E

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Fuel economy A consumer organization has reported test data for 50 car models. We will examine the asso-ciation between the weight of the car (in thousands of pounds) and the fuel efficiency (in miles per gallon).Here are the scatterplot, summary statistics, andregression analysis:Variable Count Mean StdDevMPG 50 25.0200 4.83394wt/1000 50 2.88780 0.511656Dependent variable is: MPGR-squared = 75.6%s = 2.413 with 50 - 2 = 48 dfVariable Coefficient SE(Coeff) t-ratio P-valueIntercept 48.7393 1.976 24.7 ...0.0001Weight -8.21362 0.6738 -12.2 ...0.0001 a) Is there strong evidence of an association between the weight of a car and its gas mileage? Write an appropri-ate hypothesis. b) Are the assumptions for regression satisfied?c) Test your hypothesis and state your conclusion.
SUMMARY OUTPUT Regression Statistics Multiple R 0.664798 R Square 0.441957 Adjusted R Square 0.376305 Standard Error 6.412199 Observations 20 ANOVA df SS MS F Significance F Regression 2 553.5729 276.7864 6.731793 0.007025498 Residual 17 698.9771 41.1163 Total 19 1252.55 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 26.6651 13.92768 1.91454 0.072535 -2.71974085 56.04995 X1 4.00929 1.1224 3.572068 0.002347 1.641232912 6.377348 X2 0.810165 0.477768 1.69573 0.108172 -0.19783686 1.818168 a. What can you say about the strength of this relationship for the model using the F test? Use α = .05. b. Is y significantly related to each independent variable? Use α = .05. c. Would your answer to b change if α = .001? If so, how? (3+4+3)
SUMMARY OUTPUT Regression Statistics Multiple R 0.92787098 R Square 0.86094455 Adjusted R Square 0.85807743 Standard Error 1.00347554 Observations 100 ANOVA df SS MS F Significance F Regression 2 604.745272 302.372636 300.2817255 2.78368E-42 Residual 97 97.6754268 1.006963163 Total 99 702.4206988 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 20.0921873 0.139321305 144.2147507 5.5578E-115 19.81567306 20.36870151 19.81567306 20.36870151 Education 0.50217439 0.028267255 17.76523368 2.94696E-32 0.446071712 0.558277064 0.446071712 0.558277064 Experience 0.73253401 0.042662838 17.1703067 3.64357E-31 0.64786009 0.817207937 0.64786009 0.817207937 Discuss the quality of goodness of fit of the model.
SUMMARY OUTPUT Regression Statistics Multiple R 0.92787098 R Square 0.86094455 Adjusted R Square 0.85807743 Standard Error 1.00347554 Observations 100 ANOVA df SS MS F Significance F Regression 2 604.745272 302.372636 300.2817255 2.78368E-42 Residual 97 97.6754268 1.006963163 Total 99 702.4206988 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 20.0921873 0.139321305 144.2147507 5.5578E-115 19.81567306 20.36870151 19.81567306 20.36870151 Education 0.50217439 0.028267255 17.76523368 2.94696E-32 0.446071712 0.558277064 0.446071712 0.558277064 Experience 0.73253401 0.042662838 17.1703067 3.64357E-31 0.64786009 0.817207937 0.64786009 0.817207937 What is the marginal effect of education on salary? Is…
RESEARCH ANALYSIS AND PUBLICATION (LOB7) BATCH 27 An agent for a residential real estate company in a large city would like to be able to predict the monthly rental cost for apartments, based on the size of the apartments as defined by square footage. A sample of 14 apartments in a particular residential neighbourhood was selected, and the information gathered revealed the following: APARTMENT MONTHLY RENT ($) SIZE (SQUARE FEET) 1 950 850 2 1600 1450 3 1200 1085 4 1500 1232 5 950 718 6 1700 1485 7 1650 1136 8 935 726 9 875 700 10 1150 956 11 1400 1100 12 1650 1285 13 2300 1985 14 1800 1369 Do Normality Test and Linearity test, what did you find from these tests ? explain
Palisades Eco-Park is a small ecological reserve that admits a relatively small number of visitors on any day, but provides both educational and entertaining lectures, exhibitions, and opportunities to observe nature. The company has collected the following data on labor costs and number of visitors to the park over the last 30 months. Month Labor Cost Visitors 1 $ 25,520 1,400 2 $ 35,869 2,135 3 $ 39,504 2,315 4 $ 31,280 1,830 5 $ 34,405 1,954 6 $ 32,293 1,796 7 $ 31,651 1,816 8 $ 37,889 2,204 9 $ 38,120 2,273 10 $ 46,387 2,941 11 $ 41,667 2,451 12 $ 38,576 2,309 13 $ 34,044 1,989 14 $ 32,788 1,924 15 $ 36,542 2,065 16 $ 26,192 1,729 17 $ 30,307 2,797 18 $ 29,613 2,294 19 $ 27,270 1,986 20 $ 29,654 2,448 21 $ 25,394 1,647 22 $ 24,369 1,478 23 $ 28,556 2,690 24 $ 29,803 3,144 25 $ 27,792 2,135 26 $ 31,322 2,696 27 $ 28,318 2,462 28 $ 29,579 2,512 29 $ 28,108 2,068 30 $ 27,550 1,709 Required: a. Estimate the labor…
Situation: Real Estate One conducted a recent survey of house prices for properties located on the shores of Tawas Bay. Data on 26 recent sales, including the number of bathroom, square feet and bedrooms are below. Selling Price Baths Sq Ft Beds 160000 1.5 1776 3 170000 2 1768 3 178000 1 1219 3 182500 1 1568 2 195100 1.5 1125 3 212500 2 1196 2 245900 2 2128 3 250000 3 1280 3 255000 2 1596 3 258000 3.5 2374 4 267000 2.5 2439 3 268000 2 1470 4 275000 2 1678 4 295000 2.5 1860 3 325000 3 2056 4 325000 3.5 2776 4 328400 2 1408 4 331000 1.5 1972 3 344500 2.5 1736 3 365000 2.5 1990 4 385000 2.5 3640 4 395000 2.5 1918 4 399000 2 2108 3 430000 2 2462 4 430000 2 2615 4 454000 3.5 3700 4 Action: Use the data above and…
Conduct a global test on the set of independent variables. Interpret Regression Statistics Multiple R 0.87027387 R Square 0.75737661 Adjusted R Square 0.75615535 Standard Error 14.6932431 Observations 600 ANOVA df SS MS F Significance F Regression 3 401662.063 133887.354 620.160683 8.708E-183 Residual 596 128671.271 215.891394 Total 599 530333.333 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -3.9995369 3.05935528 -1.3073137 0.19161031 -10.007965 2.00889078 -10.007965 2.00889078 Annual Income 0.0002132 3.1402E-05 6.78944156 2.7269E-11 0.00015153 0.00027487 0.00015153 0.00027487 Married 45.7808695 1.20203164 38.0862434 8.444E-162 43.4201368 48.1416023 43.4201368 48.1416023 Male 21.9175699 1.20122625 18.2459964 2.0045E-59…
A manufacturing process produces auto tires. A sample of miles at replacement is recorded below. At what milage should the warranty be set at for warranty replacement of 0.5% of the tires? Mileage at Replacement 47391 49450 52700 42709 46753 42082 53801 43975 51014 47729 49942 45216 60679 46353 52155 51420 45398 57044 51170 52729 54830 53273 48308 50209 53397 47329 54888 52780 52883 45158 48575 46073 45900 49406 47717 53113 44880 49642 47241 54091 50185 47824 54659 49625 52670 56983 44403 52557 55324 52183
Aspen Plastics produces plastic bottles to customer order. The quality inspector randomly selects four bottles from the bottle machine and measures the outside diameter of the bottleneck, a critical quality dimension that determines whether the bottle cap will fit properly. The dimensions (inch) from the last six samples are: BOTTLE Sample 1 2 3 4 1 0.694 0.622 0.698 0.69 2 0.687 0.611 0.697 0.613 3 0.671 0.680 0.695 0.602 4 0.610 0.615 0.685 0.678 5 0.680 0.624 0.618 0.614 6 0.685 0.693 0.607 0.669 What would be the upper control limit of a 3-sigma R chart? Answer:
Aspen Plastics produces plastic bottles to customer order. The quality inspector randomly selects four bottles from the bottle machine and measures the outside diameter of the bottleneck, a critical quality dimension that determines whether the bottle cap will fit properly. The dimensions (inch) from the last six samples are: BOTTLE Sample 1 2 3 4 1 0.694 0.622 0.698 0.69 2 0.687 0.611 0.697 0.613 3 0.671 0.680 0.695 0.602 4 0.610 0.615 0.685 0.678 5 0.680 0.624 0.618 0.614 6 0.685 0.693 0.607 0.669 What would be the lower control limits of a 3-sigma ?¯x¯ chart? Answer:
Aspen Plastics produces plastic bottles to customer order. The quality inspector randomly selects four bottles from the bottle machine and measures the outside diameter of the bottleneck, a critical quality dimension that determines whether the bottle cap will fit properly. The dimensions (inch) from the last six samples are: BOTTLE Sample 1 2 3 4 1 0.694 0.622 0.698 0.69 2 0.687 0.611 0.697 0.613 3 0.671 0.680 0.695 0.602 4 0.610 0.615 0.685 0.678 5 0.680 0.624 0.618 0.614 6 0.685 0.693 0.607 0.669 What would be the lower control limits of a 3-sigma ?¯x¯ chart? ( x bar chart )